
Several literature works have been presented to design incentive mechanisms for FLchain systems, blackwith the common objective of attracting more clients and datasets in AI training and improving the robustness of the overall FLchain learning [1]}, [2]}, [3]}.

Blockchain has provided blackunique features (e.g., decentralization, trustworthiness) for enabling secure incentive solutions in FL training where model updates are verified and audited by blockchain, while gradient collecting and parameter update are monitored by all participants over the ledger network, which thus ensures fairness in the FL process [4]}, [5]}.

However, the costs of verification for gradient updates during the incentive process have not been considered. In the future, it is important to analyze the tradeoff between reward profits and system costs, which benefits both clients and model owners, where auction theories can be useful for incentive analysis [6]}.

